import os
import numpy as np
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
from sklearn.cluster import KMeans

# Get data path
data_path = f"{os.path.dirname(os.getcwd())}/data"
print(data_path)

/Users/shawnguyen/Desktop/national-exam-scores-visuals/ds/data

grades = pd.read_csv(f'{data_path}/diemthi2021.csv').sort_values('CityCode')
grades

	Unnamed: 0	ID	CityCode	CityArea	Math	Literature	English	Physics	Chemistry	Biology	...	Civic Education	A	B	C	A1	D1	A-BK	A1-BK	Highest_combi	year
0	0	1000001	1	1.0	2.2	3.50	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	11.50	NaN	NaN	NaN	NaN	C	2021
67134	67455	1067456	1	1.0	5.8	5.25	2.2	NaN	NaN	NaN	...	8.25	NaN	NaN	18.75	NaN	13.25	NaN	NaN	C	2021
67133	67454	1067455	1	1.0	8.4	8.00	9.4	NaN	NaN	NaN	...	8.25	NaN	NaN	21.25	NaN	25.80	NaN	NaN	D1	2021
67132	67453	1067454	1	1.0	5.4	6.25	5.0	NaN	NaN	NaN	...	7.25	NaN	NaN	14.00	NaN	16.65	NaN	NaN	D1	2021
67131	67452	1067453	1	1.0	4.0	3.75	2.2	NaN	NaN	NaN	...	7.25	NaN	NaN	10.50	NaN	9.95	NaN	NaN	C	2021
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
997721	1016533	64002270	64	6.0	6.0	7.00	7.6	6.25	4.25	5.25	...	NaN	16.5	15.5	NaN	19.85	20.60	16.875	19.3875	D1	2021
997720	1016532	64002269	64	6.0	4.2	4.25	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2021
997719	1016531	64002268	64	6.0	8.0	6.75	6.4	NaN	NaN	NaN	...	9.00	NaN	NaN	22.00	NaN	21.15	NaN	NaN	C	2021
997717	1016529	64002266	64	6.0	4.8	8.00	3.4	NaN	NaN	NaN	...	8.75	NaN	NaN	19.00	NaN	16.20	NaN	NaN	C	2021
1002269	1021101	64006838	64	6.0	8.0	7.75	8.2	NaN	NaN	NaN	...	7.50	NaN	NaN	18.50	NaN	23.95	NaN	NaN	D1	2021

1002270 rows × 22 columns

province_grades = pd.read_csv(f'{data_path}/Province_Avg2021.csv').sort_values('CityCode')
province_grades

	CityCode	Area	Math	Literature	English	Physic	Chemistry	Biology	Geography	History	Civic Education	A	B	C	A1	D1	A-BK	A1-BK
0	1	1	6.951270	6.787373	6.463423	6.747615	6.291846	5.033329	6.777886	4.854949	8.171540	21.182955	19.502103	18.501420	21.854673	20.656833	22.001291	22.514281
49	2	5	7.160407	6.479206	7.228478	6.283695	6.533958	5.577198	7.070528	5.269874	8.588421	20.423466	19.734384	18.833810	21.456517	21.062123	21.031506	21.829240
5	3	1	6.967891	6.365921	6.508832	6.924992	6.548417	5.525145	7.144452	5.143850	8.831570	21.557381	20.164479	18.539329	22.255269	20.296301	22.233402	22.757126
31	4	3	6.848693	5.590065	6.450089	6.520510	6.452467	5.186634	6.561234	4.525579	8.011578	20.849575	19.568400	16.723309	21.416780	19.225303	21.553551	21.988156
11	5	2	4.915043	5.320990	4.173597	6.351534	6.383816	5.322719	6.394798	4.370715	7.585469	20.308195	19.394867	16.023203	20.218956	14.561329	20.929489	20.906867
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
61	60	6	6.799120	6.852512	5.851652	6.532062	6.704327	6.068955	7.336867	5.566085	8.717433	20.920321	20.466723	19.673370	20.827611	19.660974	21.452997	21.393750
62	61	6	6.307506	6.417206	5.165218	6.009154	6.195559	5.739823	7.122315	5.157759	8.650025	19.431882	19.163213	18.572888	19.120035	17.925831	19.996578	19.763931
21	62	2	5.570078	6.405598	4.486407	6.136446	6.732503	5.703914	6.865908	5.011578	8.051588	20.560667	20.191414	18.180503	19.819961	16.624004	21.203955	20.663865
42	63	4	6.174730	5.508003	4.768368	6.171119	6.360659	5.282128	6.844336	4.950118	8.005086	20.037930	19.178842	17.188822	19.385118	16.523130	20.667805	20.187190
59	64	6	6.041824	6.275926	4.589736	6.122096	6.543271	5.742384	6.984076	4.860245	8.385452	19.918906	19.594501	18.008589	18.828684	17.022804	20.373501	19.584586

63 rows × 18 columns

province_info = pd.read_excel(f'{data_path}/ProvinceInfo.xlsx').sort_values('CityCode')
display(province_info)

	CityName	CityCode	Area	Monthly Income	Province Specialized Schools	Ethnic Pupils Ratio (%)	Students/ Teacher	Urban Ratio	Poverty Rate	Covid 19
0	Hà Nội	1	1	6205.45	8	2.12	21.93	49.25	0.4904	14421
49	Thành phố Hồ Chí Minh	2	5	6536.88	2	5.59	19.25	80.10	0.0000	91981
5	Hải Phòng	3	1	5199.40	1	0.11	21.46	45.41	0.9488	233
31	Đà Nẵng	4	3	5283.63	1	0.41	17.41	87.25	0.5066	6331
11	Hà Giang	5	2	1834.55	1	77.56	16.23	15.85	26.9815	0
...	...	...	...	...	...	...	...	...	...	...
61	Bạc Liêu	60	6	3338.48	1	7.79	23.37	27.88	6.6718	51
62	Cà Mau	61	6	3034.40	1	2.41	18.36	22.71	5.9179	13
21	Điện Biên	62	2	1737.12	1	79.39	16.15	14.36	36.7358	1798
42	Đắk Nông	63	4	2808.54	1	22.85	17.52	15.28	9.0347	0
59	Hậu Giang	64	6	3974.29	1	3.08	19.38	27.97	5.5990	1

63 rows × 10 columns

def student_in_range(a, b, subject):
  subject_ab = [] 
  for citycode in sorted(list(set(grades['CityCode'].values.tolist()))):
    subject_ab.append(len(grades[(grades['CityCode']==citycode) & (grades[subject]>a) & (grades[subject]<=b)]))
  return subject_ab
  
def plot_subject(subject):
  data = province_grades[['CityCode', 'Area']]
  data['0-2'] = student_in_range(-1e-9, 2, subject)
  data['2-4'] = student_in_range(2, 4, subject)
  data['4-6'] = student_in_range(4, 6, subject)
  data['6-8'] = student_in_range(6, 8, subject)
  data['8-10'] = student_in_range(8, 10, subject)

  data['0-10'] = data['0-2']+data['2-4']+data['4-6']+data['6-8']+data['8-10']

  data['p02'] = data['0-2']/data['0-10']*100
  data['p24'] = data['2-4']/data['0-10']*100
  data['p46'] = data['4-6']/data['0-10']*100
  data['p68'] = data['6-8']/data['0-10']*100
  data['p810'] = data['8-10']/data['0-10']*100
  data = data[['CityCode', 'Area', 'p02', 'p24', 'p46', 'p68', 'p810']]
  display(data)
  fig = px.parallel_coordinates(
      data,
      color = "Area",
      labels = {'p02': '0->2', 'p24': '2->4', 'p46': '4->6', 'p68': '6->8', 'p810': '8->10'},
      color_continuous_scale=px.colors.diverging.Tealrose,
      color_continuous_midpoint=3,
      title=f"PROVINCES {subject.upper()} GRADES PROPORTION (%) DISTRIBUTION - 2021"
  )
  fig.show()
plot_subject('Math')

/var/folders/ln/rz3xh60x0zbb7cfm8z1lrlm40000gn/T/ipykernel_81620/2560017616.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  data['0-2'] = student_in_range(-1e-9, 2, subject)
/var/folders/ln/rz3xh60x0zbb7cfm8z1lrlm40000gn/T/ipykernel_81620/2560017616.py:10: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  data['2-4'] = student_in_range(2, 4, subject)
/var/folders/ln/rz3xh60x0zbb7cfm8z1lrlm40000gn/T/ipykernel_81620/2560017616.py:11: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  data['4-6'] = student_in_range(4, 6, subject)
/var/folders/ln/rz3xh60x0zbb7cfm8z1lrlm40000gn/T/ipykernel_81620/2560017616.py:12: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  data['6-8'] = student_in_range(6, 8, subject)
/var/folders/ln/rz3xh60x0zbb7cfm8z1lrlm40000gn/T/ipykernel_81620/2560017616.py:13: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  data['8-10'] = student_in_range(8, 10, subject)
/var/folders/ln/rz3xh60x0zbb7cfm8z1lrlm40000gn/T/ipykernel_81620/2560017616.py:15: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  data['0-10'] = data['0-2']+data['2-4']+data['4-6']+data['6-8']+data['8-10']
/var/folders/ln/rz3xh60x0zbb7cfm8z1lrlm40000gn/T/ipykernel_81620/2560017616.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  data['p02'] = data['0-2']/data['0-10']*100
/var/folders/ln/rz3xh60x0zbb7cfm8z1lrlm40000gn/T/ipykernel_81620/2560017616.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  data['p24'] = data['2-4']/data['0-10']*100
/var/folders/ln/rz3xh60x0zbb7cfm8z1lrlm40000gn/T/ipykernel_81620/2560017616.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  data['p46'] = data['4-6']/data['0-10']*100
/var/folders/ln/rz3xh60x0zbb7cfm8z1lrlm40000gn/T/ipykernel_81620/2560017616.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  data['p68'] = data['6-8']/data['0-10']*100
/var/folders/ln/rz3xh60x0zbb7cfm8z1lrlm40000gn/T/ipykernel_81620/2560017616.py:21: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  data['p810'] = data['8-10']/data['0-10']*100

	CityCode	Area	p02	p24	p46	p68	p810
0	1	1	0.721498	8.432819	17.747246	42.346219	30.752219
49	2	5	0.066356	2.196740	16.343423	57.450524	23.942957
5	3	1	0.499573	6.806149	18.714774	45.550811	28.428693
31	4	3	0.956633	7.900191	18.654337	47.297513	25.191327
11	5	2	2.997624	35.660757	34.271614	20.928532	6.141473
...	...	...	...	...	...	...	...
61	60	6	0.081460	3.861193	23.395243	56.630824	16.031281
62	61	6	0.324314	9.368050	31.773536	47.081171	11.452928
21	62	2	0.898001	19.942392	41.579126	30.430363	7.150119
42	63	4	0.608921	13.122241	31.405084	41.802405	13.061349
59	64	6	0.356453	12.475865	35.972078	42.714986	8.480618

63 rows × 7 columns

plot_subject('English')

/var/folders/ln/rz3xh60x0zbb7cfm8z1lrlm40000gn/T/ipykernel_81620/2560017616.py:9: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/var/folders/ln/rz3xh60x0zbb7cfm8z1lrlm40000gn/T/ipykernel_81620/2560017616.py:10: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/var/folders/ln/rz3xh60x0zbb7cfm8z1lrlm40000gn/T/ipykernel_81620/2560017616.py:11: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/var/folders/ln/rz3xh60x0zbb7cfm8z1lrlm40000gn/T/ipykernel_81620/2560017616.py:12: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/var/folders/ln/rz3xh60x0zbb7cfm8z1lrlm40000gn/T/ipykernel_81620/2560017616.py:13: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/var/folders/ln/rz3xh60x0zbb7cfm8z1lrlm40000gn/T/ipykernel_81620/2560017616.py:15: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/var/folders/ln/rz3xh60x0zbb7cfm8z1lrlm40000gn/T/ipykernel_81620/2560017616.py:17: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/var/folders/ln/rz3xh60x0zbb7cfm8z1lrlm40000gn/T/ipykernel_81620/2560017616.py:18: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/var/folders/ln/rz3xh60x0zbb7cfm8z1lrlm40000gn/T/ipykernel_81620/2560017616.py:19: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/var/folders/ln/rz3xh60x0zbb7cfm8z1lrlm40000gn/T/ipykernel_81620/2560017616.py:20: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/var/folders/ln/rz3xh60x0zbb7cfm8z1lrlm40000gn/T/ipykernel_81620/2560017616.py:21: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

	CityCode	Area	p02	p24	p46	p68	p810
0	1	1	1.717851	24.010917	17.763354	19.101626	37.406252
49	2	5	0.315108	8.815115	18.053450	30.204227	42.612101
5	3	1	0.880827	17.945055	24.558396	24.029900	32.585821
31	4	3	1.310198	19.900850	21.804178	24.212110	32.772663
11	5	2	5.951668	55.322548	23.746755	8.647893	6.331136
...	...	...	...	...	...	...	...
61	60	6	0.437139	17.415632	40.304249	28.309145	13.533835
62	61	6	1.382183	32.007952	37.621888	19.757645	9.230332
21	62	2	1.666036	46.989777	36.842105	8.670958	5.831125
42	63	4	3.020188	42.997934	30.980766	13.876967	9.124146
59	64	6	2.966102	45.856874	31.732580	13.214062	6.230383

63 rows × 7 columns

Math

• Provinces in region 6, the percentage of candidates who are weak in math is relatively low, and scores from 6-8 are relatively high.
• In some provinces in region 2, the number of students who are weak in math is 2-3 times higher than in other provinces, along with that, the number of students who get 6-8 is not high.
• Areas 5, 6 tend to be the same.

English

• English scores are clearly divided by region, especially for the top candidates. The most prominent are the contestants from areas 1 and 6.
• Meanwhile, in Region 2, there are still provinces with a high percentage of weak candidates.

data = province_info[['Monthly Income', 'Province Specialized Schools', 'Ethnic Pupils Ratio (%)', 'Students/ Teacher', 'Urban Ratio', 'Poverty Rate']]
normalized_data = (data-data.min())/(data.max()-data.min())
X = normalized_data.values
X.shape

(63, 6)

kmeans  = KMeans(n_clusters = 2, init = 'random', n_init = 10, tol = 1e-3, random_state = 2021).fit(X)
normalized_data[['CityCode']] = province_info[['CityCode']]
normalized_data['Label'] = kmeans.labels_
province_grades['Label'] = kmeans.labels_
province_info['Label'] = kmeans.labels_
normalized_data[normalized_data['Label'] == 0]

	Monthly Income	Province Specialized Schools	Ethnic Pupils Ratio (%)	Students/ Teacher	Urban Ratio	Poverty Rate	CityCode	Label
11	0.018395	0.0	0.813629	0.170874	0.078115	0.734474	5	0
12	0.084504	0.0	1.000000	0.120388	0.178696	0.706815	6	0
22	0.031660	0.0	0.828013	0.281553	0.099419	0.839367	7	0
15	0.130256	0.0	0.625472	0.299029	0.179600	0.418671	8	0
14	0.178205	0.0	0.602373	0.339806	0.051904	0.351238	9	0
18	0.105612	0.0	0.889857	0.139806	0.170949	0.333781	10	0
13	0.085850	0.0	0.941201	0.164078	0.161007	0.584378	11	0
16	0.148271	0.0	0.488975	0.572816	0.136346	0.410077	13	0
23	0.001490	0.0	0.828538	0.736893	0.052421	0.831042	14	0
24	0.183159	0.0	0.745170	0.205825	0.181407	0.247388	23	0
39	0.120478	0.0	0.379357	0.000000	0.296191	0.599794	36	0
21	0.000000	0.0	0.832843	0.163107	0.058877	1.000000	62	0

labels_list = list(kmeans.labels_)
print(labels_list)
print(labels_list.count(0))
print(labels_list.count(1))
display(province_info[['CityCode', 'Label']][province_info['Label']==1])

[1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1]
12
51

	CityCode	Label
0	1	1
49	2	1
5	3	1
31	4	1
17	12	1
20	15	1
1	16	1
3	17	1
19	18	1
2	19	1
4	21	1
6	22	1
8	24	1
9	25	1
7	26	1
10	27	1
25	28	1
26	29	1
27	30	1
28	31	1
29	32	1
30	33	1
32	34	1
33	35	1
34	37	1
40	38	1
35	39	1
41	40	1
36	41	1
43	42	1
44	43	1
46	44	1
37	45	1
45	46	1
38	47	1
47	48	1
50	49	1
55	50	1
56	51	1
48	52	1
51	53	1
57	54	1
58	55	1
52	56	1
54	57	1
53	58	1
60	59	1
61	60	1
62	61	1
42	63	1
59	64	1

def plot_radar(data, range_x):
  categories = list(data.columns)
  fig = go.Figure()
  for i in range(data.shape[0]):
    fig.add_trace(go.Scatterpolar(
        r=list(data.loc[i].values),
        theta=categories,
        fill='toself',
        name=f'Label {i}'
    ))
  fig.update_layout(
    polar=dict(
      radialaxis=dict(
        visible=True,
        range=range_x,
      )),
    showlegend=False
  )
  fig.show()

radar_data_1 = normalized_data.drop(['CityCode', 'Province Specialized Schools'], axis=1).groupby('Label').mean()
display(radar_data_1)
plot_radar(radar_data_1, [0,1])

	Monthly Income	Ethnic Pupils Ratio (%)	Students/ Teacher	Urban Ratio	Poverty Rate
Label
0	0.090657	0.747953	0.266181	0.137078	0.588085
1	0.421242	0.078608	0.420864	0.299103	0.116899

radar_data_2 = province_grades.drop(["CityCode", "Area", 'A', 'B', 'C', 'A1', 'D1', 'A-BK', 'A1-BK'], axis=1).groupby('Label').mean()
display(radar_data_2)
plot_radar(radar_data_2, [0,9])

	Math	Literature	English	Physic	Chemistry	Biology	Geography	History	Civic Education
Label
0	5.659077	6.059589	4.819625	6.499246	6.63131	5.647756	6.881958	4.919888	8.227905
1	6.614581	6.438082	5.750278	6.580169	6.67044	5.573001	6.994920	5.007699	8.417800